[1]张馨匀,周琳家,程煜婷,等.基于伪标签细化的域适应TSK模糊分类器[J].智能系统学报,2025,20(3):557-570.[doi:10.11992/tis.202408015]
ZHANG Xinyun,ZHOU Linjia,CHENG Yuting,et al.Domain adaptive Takagi-Sugeno-Kang fuzzy classifier based on pseudo-label refinement[J].CAAI Transactions on Intelligent Systems,2025,20(3):557-570.[doi:10.11992/tis.202408015]
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ZHANG Xinyun,ZHOU Linjia,CHENG Yuting,et al.Domain adaptive Takagi-Sugeno-Kang fuzzy classifier based on pseudo-label refinement[J].CAAI Transactions on Intelligent Systems,2025,20(3):557-570.[doi:10.11992/tis.202408015]
基于伪标签细化的域适应TSK模糊分类器
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
20
期数:
2025年第3期
页码:
557-570
栏目:
学术论文—机器学习
出版日期:
2025-05-05
- Title:
- Domain adaptive Takagi-Sugeno-Kang fuzzy classifier based on pseudo-label refinement
- Keywords:
- domain adaptation; Takagi-Sugeno-Kang fuzzy classifier; random walking; pseudo-label refinement; fuzzy shared feature space; unsupervised learning; fuzzy rule; transfer learning
- 分类号:
- TP391
- 摘要:
- Takagi-Sugeno-Kang (TSK)模糊分类器由于其良好的分类性能和可解释性在多个领域有着广泛的应用。针对训练样本和测试样本分布差异所导致的TSK模糊分类器泛化性能下降问题提出了一种基于伪标签细化的域适应TSK模糊分类器。该分类器使用模糊规则前件的非线性映射和后件的线性映射能力构建源域和目标域数据的模糊共享特征空间，并在模糊共享特征空间采用基于图随机游走和标签过滤细化两种策略来提升目标域伪标签质量来更好地进行域对齐。通过在多个公开数据集上的广泛实验，验证了所提出的域适应TSK模糊分类器不仅具备可靠的分类性能，还具有良好的可解释性。
- Abstract:
- The Takagi-Sugeno-Kang (TSK) fuzzy classifier (FC) has been widely applied to various fields owing to its excellent classification performance and interpretability. To address the degradation of the generalization performance of this TSK TSK FC caused by the differences in the distributions of the training and test samples, a domain adaptive (DA) pseudo-label refinement (PLR)-based TSK FC (DA-TSK-PLR-FC) is proposed. This classifier leverages the nonlinear and linear mapping capabilities of the antecedent and consequent parts in fuzzy rules to construct a fuzzy shared feature space for source and target domain data. In this fuzzy shared feature space, graph-based random walking and label filtering refinement were applied to enhance the pseudo-label quality in the target domain, thereby enhancing the effectiveness of the domain alignment. Further, extensive experiments using multiple public datasets reveal that the proposed DA-TSK-PLR-FC achieves reliable classification performance and good interpretability.
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备注/Memo
收稿日期:2024-8-18。
基金项目:江苏省研究生研究实践创新计划项目(KYCX24_3561)；中国博士后科学基金资助项目（2023T160342）；国家自然科学基金项目(82072019).
作者简介:张馨匀，硕士研究生，主要研究方向为智能医学工程。E-mail：2231310043@stmail.ntu.edu.cn。;周琳家，主要研究方向为智能医学工程。E-mail：2131110559@stmail.ntu.edu.cn。;张远鹏，教授，博士，2019 届香江学者，江苏省人工智能协会不确定性人工智能专业委员会委员，IEEE 会员，TCYB、TNNLS、TFS、SMCA、TCBB 等权威期刊的审稿人和客座编委。主要研究方向为人工智能与模式识别(模糊聚类、TSK 模糊系统、特征选择等)及其在医学上 (脑电信号处理、多模态影像组学分析) 的应用。主持国家自然科学基金项目 2 项、江苏省自然科学基金项目 1 项、江苏省博士后基金项目 1 项、南通市科技计划项目 1 项。发表学术论文 30 篇。E-mail：y.p.zhang@ieee.org。
通讯作者:张远鹏. E-mail：y.p.zhang@ieee.org
更新日期/Last Update:
1900-01-01